Ph.D. in Operations Research / Big Tech Eng: How to transition into intermediate/advanced ML for high-value industries (Robotics, Defense, Finance)? [D]

Core Interests

Forecasting, predictive analytics, and machine learning applied to industrial settings.

Target Industries

• Robotics/Autonomous Systems
• Defense/Aerospace
• Quantitative Finance

What I Want to Skip

I have little interest in doing core NLP/LLM research, though I am interested in RL, Multi-Agent systems, and applied AI.

Where I Am Right Now

I have a solid grasp of optimization and basic/intermediate ML/stats. However, I want to bridge the gap into more intermediate/advanced ML topics that are actually useful and highly valued by employers. I want to get back into heavy math, but only if it drives real-world business value.

What I'm Looking to Learn

• Causal Inference: (e.g., Structural Causal Models, Uplift modeling, Double ML)
• Tree-Based Math: Understanding things like XGBoost from the ground up (deriving gradients/hessians for custom loss functions, implementing from scratch)
• Reinforcement Learning / Control: Bridging the gap between OR dynamic programming and deep RL for robotics/defense

My Questions for the Community

Skill Prioritization

From a purely market-driven, high-compensation perspective, which specific ML topics should a Ph.D. in OR focus on to stand out in Robotics, Defense, or Banking/Finance?

Portfolio/Proof

How can I best demonstrate to employers that I have the engineering chops to implement these advanced models from scratch, rather than just calling APIs?

Positioning

How do I best market the "Predict-then-Optimize" sweet spot (combining ML predictions with OR optimization frameworks) to companies in these sectors?

Would love any advice on textbooks, specific frameworks to master, or strategies on how to position my background for maximum leverage. Thanks!